Resumo

CERVANTES-OSORNIO, Rocío; ARTEAGA-RAMIREZ, Ramón; VAZQUEZ-PENA, Mario Alberto  e  QUEVEDO-NOLASCO, Abel. Artificial neural networks in the estimation of reference evapotranspiration. Rev. Mex. Cienc. Agríc [online]. 2011, vol.2, n.3, pp.433-447. ISSN 2007-0934.

Artificial neural networks represent a vast research field, since they have demonstrated application in various fields of science. Its ability to cope with nonlinearities in several different phenomena and work in the estimation or forecast meteorological variables, which act directly and indirectly in reference evapotranspiration and actual evapotranspiration, have led to this work development. The aim was to present a literature review on artificial neural networks for reference evapotranspiration estimating and related variables, including: theory and artificial neural networks foundations and backpropagation algorithm, some similarities and differences between traditional statistical models and artificial neural networks, applications of artificial neural networks in reference evapotranspiration estimating and variables associated with the prospects of artificial neural networks in agroclimatic variables prediction. Static neural multilayer networks, are so far the most common in reference evapotranspiration estimation and a change in applying artificial neural networks of dynamic type trend looms.

Palavras-chave : modeling; meteorological variables; prediction.